Application of Hierarchical Clustering to Analyze Solvent-Accessible Surface Area Patterns in Amycolatopsis lipases.

Abstract

Simple SummarySolvent-Accessible Surface Area (SASA) as the one dimensional structure property of the protein considers as the measuring the exposure of an amino acid residue to the solvent in one protein. It is an important structural property as the active sites of proteins are mostly located on the protein surfaces. The aim of this paper is to provide the clear information on different Amycolatopsis eburnea lipases based on the SASA patterns. This information could help in recognizing the structural stability and conformation as well as precise clustering them for revealing lipase evolution.The wealth of biological databases provides a valuable asset to understand evolution at a molecular level. This research presents the machine learning approach, an unsupervised agglomerative hierarchical clustering analysis of invariant solvent accessible surface areas and conserved structural features of Amycolatopsis eburnea lipases to exploit the enzyme stability and evolution. Amycolatopsis eburnea lipase sequences were retrieved from biological database. Six structural conserved regions and their residues were identified. Total Solvent Accessible Surface Area (SASA) and structural conserved-SASA with unsupervised agglomerative hierarchical algorithm were clustered lipases in three distinct groups (99/96%). The minimum SASA of nucleus residues was related to Lipase-4. It is clearly shown that the overall side chain of SASA was higher than the backbone in all enzymes. The SASA pattern of conserved regions clearly showed the evolutionary conservation areas that stabilized Amycolatopsis eburnea lipase structures. This research can bring new insight in protein design based on structurally conserved SASA in lipases with the help of a machine learning approach.